Mental and Behavioral Factors Associated With Food Addiction Among University Students: A Bangladeshi Study.
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| Title: | Mental and Behavioral Factors Associated With Food Addiction Among University Students: A Bangladeshi Study. |
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| Authors: | Das, Pronab (AUTHOR), Al-Mamun, Firoj (AUTHOR), Hasan, Md Emran (AUTHOR), Islam, Johurul (AUTHOR), Roy, Nitai (AUTHOR), ALmerab, Moneerah Mohammad (AUTHOR), Muhit, Mohammad (AUTHOR), Gozal, David (AUTHOR), Mamun, Mohammed A. (AUTHOR), Ramos-Pichardo, Juan Diego (AUTHOR) |
| Source: | Perspectives in Psychiatric Care. 10/13/2025, Vol. 2025, p1-18. 18p. |
| Subjects: | Competency assessment (Law), Cross-sectional method, Substance abuse, Research funding, Academic medical centers, Cronbach's alpha, Statistical sampling, Questionnaires, Multiple regression analysis, Sex distribution, Smoking, Insomnia, Descriptive statistics, Chi-squared test, Anxiety, Surveys, Odds ratio, Health behavior, Research, Pornography, Psychological stress, Compulsive eating, Psychology of college students, Machine learning, Data analysis software, Alcohol drinking, Confidence intervals, Mental depression |
| Geographic Terms: | Bangladesh |
| Abstract: | Background: Food addiction, characterized by the compulsive consumption of highly palatable foods, poses significant health risks, particularly among university students. This study investigates the prevalence of food addiction among Bangladeshi university students and its associations with mental health (depression, anxiety, stress, and insomnia) and behavioral factors (smoking, drug, alcohol use, and pornography consumption). Machine learning (ML) models were applied to enhance predictive accuracy. Methods: A cross‐sectional survey was conducted among 1697 participants across two Bangladeshi universities. Food addiction was assessed using the Modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Associations were examined using logistic regression and subgroup analyses by gender. Six ML models—K‐nearest neighbors (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), XGBoost, and CatBoost—were employed to improve classification performance. Results: Overall, 13% of students met the criteria for food addiction, with higher prevalence among males (14.8%) than females (10.4%). In adjusted models, anxiety (AOR = 2.44, 95% CI: 1.43–4.16), stress (AOR = 1.74, 95% CI: 1.18–2.58), and pornography use (AOR = 1.74, 95% CI: 1.12–2.69) were significant predictors. Subgroup analyses showed that anxiety, stress, and pornography use were significant predictors only among males. Among ML models, KNN achieved the highest accuracy (85.3%), while RF demonstrated the best AUC‐ROC (0.697), confirming their utility in identifying at‐risk individuals. Conclusions: Food addiction affects a notable proportion of Bangladeshi university students and is strongly linked with anxiety, stress, and pornography use, particularly among males. Interventions should include cognitive‐behavioral therapy and stress management programs, digital hygiene education, and nutritional counseling tailored to student populations. ML‐based predictive models, such as RF and CatBoost, may be integrated into campus health systems to support early identification and personalized interventions. [ABSTRACT FROM AUTHOR] |
| Copyright of Perspectives in Psychiatric Care is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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